'''simple docstring'''






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   1
   @register_to_config
   def __init__(      self      :			List[str], UpperCamelCase__      :			int = 10_00, UpperCamelCase__      :			Optional[Union[np.ndarray, List[float]]] = None     )  ->			str:
        # set `betas`, `alphas`, `timesteps`
        self.set_timesteps(UpperCamelCase__     )

        # standard deviation of the initial noise distribution
        _A    		=  1.0

        # For now we only support F-PNDM, i.e. the runge-kutta method
        # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
        # mainly at formula (9), (12), (13) and the Algorithm 2.
        _A    		=  4

        # running values
        _A    		=  []
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			Union[str, torch.device] = None     )  ->			Dict:
        _A    		=  num_inference_steps
        _A    		=  torch.linspace(1, 0, num_inference_steps + 1     )[:-1]
        _A    		=  torch.cat([steps, torch.tensor([0.0]     )]     )

        if self.config.trained_betas is not None:
             _A    		=  torch.tensor(self.config.trained_betas, dtype=torch.floataa     )
        else:
             _A    		=  torch.sin(steps * math.pi / 2     ) ** 2

        _A    		=  (1.0 - self.betas**2) ** 0.5

        _A    		=  (torch.atana(self.betas, self.alphas     ) / math.pi * 2)[:-1]
        _A    		=  timesteps.to(UpperCamelCase__     )

        _A    		=  []
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			torch.FloatTensor, UpperCamelCase__      :			int, UpperCamelCase__      :			torch.FloatTensor, UpperCamelCase__      :			bool = True, )  ->			Union[SchedulerOutput, Tuple]:
        if self.num_inference_steps is None:
             raise ValueError(
                 'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

        _A    		=  (self.timesteps == timestep).nonzero().item()
        _A    		=  timestep_index + 1

        _A    		=  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
        self.ets.append(UpperCamelCase__     )

        if len(self.ets     ) == 1:
             _A    		=  self.ets[-1]
        elif len(self.ets     ) == 2:
             _A    		=  (3 * self.ets[-1] - self.ets[-2]) / 2
        elif len(self.ets     ) == 3:
             _A    		=  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
        else:
             _A    		=  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

        _A    		=  self._get_prev_sample(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			torch.FloatTensor, *UpperCamelCase__      :			Optional[int], **UpperCamelCase__      :			Dict     )  ->			torch.FloatTensor:
        return sample
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			Any, UpperCamelCase__      :			int, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  self.alphas[timestep_index]
        _A    		=  self.betas[timestep_index]

        _A    		=  self.alphas[prev_timestep_index]
        _A    		=  self.betas[prev_timestep_index]

        _A    		=  (sample - sigma * ets) / max(UpperCamelCase__, 1e-8     )
        _A    		=  next_alpha * pred + ets * next_sigma

        return prev_sample

   def __len__(      self      :			int     )  ->			Dict:
        return self.config.num_train_timesteps




from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
